NIPS 2017：贝叶斯深度学习与深度贝叶斯学习（讲义+视频）

点击上方 “机器学习研究会”可以订阅

摘要

 

转自：爱可可-爱生活

Probabilistic and Bayesian reasoning is one of the principle theoretical pillars to our understanding of machine learning. Over the last two decades, it has inspired a whole range of successful machine learning methods and influenced the thinking of many researchers in the community. On the other hand, in the last few years the rise of deep learning has completely transformed the field and led to a string of phenomenal, era-defining, successes. In this talk I will explore the interface between these two perspectives on machine learning, and through a number of projects I have been involved in, explore questions like: How can probabilistic thinking help us understand deep learning methods or lead us to interesting new methods? Conversely, how can deep learning technologies help us develop advanced probabilistic methods?

 

链接：

http://csml.stats.ox.ac.uk/news/2017-12-08-ywteh-breiman-lecture/

原文链接：

https://m.weibo.cn/1402400261/4183343342416996

“完整内容”请点击【阅读原文】

↓↓↓